Issue 67

S. Sahu, Frattura ed Integrità Strutturale, 67 (2024) 12-23; DOI: 10.3221/IGF-ESIS.67.02

Step8: Then the mutated clones are added to the search space or the initial population (P n ) and reselect ‘n s2 ’ of the optimized clones. Then these are added to the memory cells (M) of the immunity cells. Step9: Step 2-8 are repeated till the end conditions are not met. The termination conditions may vary from problem to problem. In this case, the numbers of iterations (20) are taken as the terminating condition.

A PPLICATION OF REGRESSION ANALYSIS FOR STRUCTURAL DAMAGE DETECTION

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ata mining is the process of discovering or finding out the potentially useful data or information from the database. This has been successfully applied in many fields other than engineering also. In the proposed methodology, data mining used to make a data base for the proposed algorithm. So, the relation between the variables should be known. So, for the above mentioned reasons Linear Regression Analysis has been used. Regression Analysis describes the relationship between a response (dependent) variable and explanatory (independent) variables. Once the relationship is established that can be used for future assessment of relationship between variables. In general, we can say regression analysis gives the best guess while making a kind of prediction. In real life problems, there may be number of input and output variables. So, simple regression analysis (RA) may not be enough to predict the relationship between the independent and dependent variables. The Fig.3 depicts the classification of Regression Analysis (RA). This can also be used to predict the future value of the dependent variable using the established relationship. The two main distinct purposes are given below. 1. Regression Analysis (RA) is mainly used for forecasting and prediction. 2. This can be used for inferring a predictable relationship between the dependent and independent variables. y=a+bx+e (14)

where, a=Dependent variable, b=Slope, x=Independent variable, e=Residual error

Figure 3: Classification of Regression Analysis

A PPLICATION OF HYBRIDIZED CSA AND REGRESSION ANALYSIS

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he theory of Clonal Selection Algorithm (CSA) is based on the coping of the immune cells. So, during the application of Clonal Selection Algorithm for optimization with the characteristics of CSA, the conventional CSA method usually suffers premature convergence and trapping of the algorithm in local solutions which will lead to improper solution. To address the concerning the loopholes, a statistical method has been incorporated in the conventional method. Most of optimization methods including CSA are used to train the data from the data acquisition methods. Usually during the collection of data, errors are induced in the data. These errors are also known as residual errors. These errors will lead to trapping of the algorithm in local solutions. So, to avoid this condition a statistical method has been incorporated. Here, Regression Analysis is applied to find the correlation between the dependent and independent variables. Usually, it has been observed that the patterns or methods used for the prediction of the outcome data from the solution space or data pool. As earlier mentioned, the collection of data contains the residual error. It is difficult to establish any relation between the input and output variables. So, to establish the relationship between the input and output variables and to reduce the effect of the residual error on the prediction of solutions, the analyses are done. Due to the above-mentioned reasons, the data generated from the data acquisition methods are trained in the Regression Analysis. After the training of data in the Regression Analysis, the trained data are trained in Clonal Selection Algorithm.

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